What the interviews show. In Alium's verified buyer interviews, neither Sierra nor Decagon emerges as an overall winner, and the two are not evidenced equally. Sierra is live at retailers, a bank, health insurers and travel companies, a few of them for a year or more; its buyers emphasize a collaborative vendor team, accurate answers and tools that show where the agent falls short, and their complaints are the effort it takes to run, slow progress from containing conversations to resolving them, and cost. Decagon is live at only a few companies with an account of it here; they emphasize its support and analytics, one says those analytics do not always show a true resolution rate, and a company that piloted it says it over-promised. A few companies weigh both: one chose Sierra for chat, one chose neither and built on its existing tools, and the rest are still evaluating. Nobody here tests the two on the same conversations, and the outcome figures buyers give are mostly containment, the share of conversations that never reach a person, which is not the same as a verified resolution. The split, as far as this evidence goes, is a longer production record on one side against a few strong first-year accounts on the other.
| Strongest contrast in these interviews | A longer production record: chat and voice agents live at many companies, some for a year or more | A few accounts: live users under two years in, and one company that piloted it and walked away |
|---|---|---|
| Buyer pattern in these interviews | Large retailers, a bank, health insurers and travel companies, often running it beside Salesforce, Genesys or another platform | A consumer brand and a travel company running it live; banks evaluating it, and a hospitality company that piloted it |
| Most-discussed job | Customer-facing AI agent for chat and voice, both described live here | Customer-facing AI agent, described here mostly for chat and knowledge questions; Decagon also sells voice agents |
| Also does | Buyers use its conversation review and Explorer tooling to find where containment fails, and one retailer runs it for on-site conversational search | Buyers use its reporting, including Watchtower dashboards; one live user plans to add voice and actions such as refunds, and a bank evaluating it says it covers automated QA “to a very small degree”. The interviews do not show how its voice or actions perform |
| Praise in these interviews | The vendor team, from a buyer who calls it “very collaborative” | Support and analytics, from users who cite “fantastic support” and “Very good analytics” |
| Complaints in these interviews | The effort to run it, and slow progress from containment to resolving a material share of requests | Analytics that do not always show a true resolution rate, and a pilot one company says over-promised |
| Buyer-reported cost | One buyer says it is “not the most budget-friendly”; others got a discounted pilot or found it cost-effective | One user calls its pricing very competitive; a company that piloted it says the cost structure did not fit |
| Operating pattern in these interviews | Teams that staff the agent like a channel, reviewing conversations and fixing gaps week by week | Teams answering knowledge questions first, with actions and voice planned for later |
What buyers say about each platform
Every buyer interview spends its minutes somewhere — praising the thing that won the deal, or flagging the thing that still stings. Map where those minutes go for Sierra and Decagon, and the chart below shows where each vendor's praise and complaints concentrate in these interviews — not a ranking of what either can do:
Relative share of buyer commentary by theme in Alium's verified interviews, praise vs. complaint, through November 2026. Widths compare themes within this pair — they are relative, not counts. An empty bar means no buyer raised that theme.
In their own words
The phrases buyers reach for, verbatim, when they describe each platform:
Sierra
“very collaborative” team
“very few examples of hallucinations” accuracy
“The implementation is fast.” rollout
“not the most budget-friendly” cost
“They have a lot of bugs.” quality
“the level of effort that is required to manage it day-to-day” upkeep
Decagon
“fantastic support” team
“Very good analytics” reporting
“very competitive pricing” cost
“doesn't always get to a true resolution rate” resolution
“they over promised” pilot
“The cost structure did not fit well with us.” cost
Where Sierra buyers concentrate their praise
Sierra buyers praise a collaborative vendor team, accurate answers and tools that show where the agent falls short. Their recurring complaints are the effort it takes to run, slow progress from containing conversations to resolving them, bugs at one company, and cost. Most of the companies describing it run it live. A US restaurant chain chose it after looking at five agent vendors and running a pilot, and its customer-experience manager says “The implementation is fast,” that the vendor's team fixes issues raised in its review tool and “They give us a weekly status update.” An online marketplace that vetted “at least a dozen” vendors chose it because it “was able to demonstrate that their product is being used by some of the big players,” and says “We have seen very few examples of hallucinations or incorrect usage of our policies.” A large health insurer, live since June 2026, values the team's knowledge of healthcare and Explorer, which it says “allows them to tell us, as the consumer, where and how we can improve our containment rate.” A specialty retailer runs it in voice and chat, has added some French, and is “working on getting the internal bots turned off and just using Sierra as that front door.” A leisure and hospitality company chose it after piloting three or four others, and an Asian airline after a paid proof of concept.
The complaints are mostly about what it takes to get from answering to resolving. The online marketplace, customer-facing since August 2026, was “a little surprised at the level of effort that is required to manage it day-to-day,” says running it well takes “a basic understanding of back-end engineering and product,” and is holding its view “until I start to see a material amount of resolutions being handled by the bot.” A subscription e-commerce company, live and weighing whether to open an RFP at renewal, says “most of the optimizations need to come from our side,” that “They have a lot of bugs,” and that “it feels like we're training the tool more so than we're getting out of the tool,” while crediting a team that “actually gets stuff done fairly quickly.” A consumer health company that adopted it in 2025 says “it's only as good as the data you input into it,” and a health-benefits organization a year in names adoption as the challenge. These describe particular deployments and dates, not what the product can do today. Sierra describes Explorer, introduced in an April 2026 post, as an agent that researches customer conversations to suggest what to fix in the agent. It is aimed at this work, but a diagnosis is not a verified outcome, and these accounts do not show how much it reduces the effort.
Where Decagon buyers concentrate their praise
Decagon's live users with an account of it praise its support, its analytics and, at one of them, its pricing. The complaints come from one live user, about measuring resolution, and from one company that piloted it. That is a few accounts, not a pattern. A European consumer brand has run it as a customer-facing agent since September 2025 and, after proofs of concept with other vendors, says “Decagon was really at the top of everything.” It chose Decagon for “the fantastic support,” saying “We did iterations within five minutes of sending the cases to them,” for dashboards and reports in its Watchtower environment, “which is something the other vendors did not have,” and for “very competitive pricing.” It also checked a reference: “I checked with a company that was already a customer of Decagon.” Today the agent answers knowledge questions such as order status; the brand plans to move “to solving issues, so that the AI agent can also do refunds, compensations, resends,” and names the risk if that does not happen quickly enough. Decagon also sells voice agents and introduced its Voice 3 agent in an October 2026 post; the brand wants to use Decagon Voice but has not yet, and no live user here describes running it. Decagon describes Watchtower, introduced in a June 2025 post, as always-on quality review of every conversation, AI or human, against criteria the customer sets, filterable by whether a conversation was resolved or escalated. That can surface problems at scale, but a QA result is not proof that a customer's problem was solved, which is the point the travel company below makes about Decagon's analytics.
A travel company running it for seven months beside a homegrown tool chose it because “It looked to be as good as any other one,” and praises its analytics. Its complaint is about measurement: the analytics do not always show a true resolution rate, so it worked out its own from internal data. A global hospitality company piloted Decagon and turned it down: “They promised too much, but the delivery was very low in that sense,” and “The cost structure did not fit well with us.” That is one pilot. A complaint beside a decision did not necessarily cause it.
What happened at companies that weighed both? A few companies weigh Sierra and Decagon directly, and they ended in different places, so the moves below are classified one company at a time. Selected Sierra: a large online retailer evaluated Decagon among several tools, chose Sierra for chat self-service and contact deflection after a proof of concept with a couple of journeys live, and confirmed later that year that “We signed the deal as well with Sierra.” For voice it was still weighing both: “Sierra is our front-runner right now. Decagon is somebody we are looking at as well from the voice side.” Chose neither: the global hospitality company called them “the biggest two that we're in the market looking at,” piloted Decagon, evaluated Sierra, and says of Sierra “their product delivered what we were expecting” but that cost did not fit its long-term strategy. It decided “to test out AgentForce and build our own.” Incumbent plus an open evaluation: a large North American bank has run Sierra for two years and wants “end-to-end resolution, not just chatbot deflection. Sierra has parts of it, but we're also looking into Decagon, Cresta, and Cognigy”; it has not shortlisted. Early research: a US consumer bank is researching both alongside Cresta and others, weighing integration with its Genesys and Salesforce stack, whether it can “bring our own large language model,” and voice latency. That is too few companies to say which vendor buyers prefer, and no company here reports moving from one to the other.
Is containment the same as resolution? No, and the buyers closest to these products say so. Containment, or deflection, is the share of conversations that end without reaching a person; a verified resolution means the customer's problem was actually solved. The clearest outcome figure in these interviews measures containment: a US restaurant chain says its chat containment rose from about 30% to more than 70% after moving off a decision-tree bot, with customer satisfaction on contained chats matching that for human agents, by its account. That is the buyer's own reading, and it measures conversations kept from agents, not problems confirmed solved. Other buyers draw the line explicitly. A large US retailer is piloting Sierra because “containment doesn't always translate to improved CSAT,” and says the deciding factor will be “their ability to customize the criteria for how you classify a contact as issue resolved rather than just contained”; its decision is expected early next year. The Decagon user at the travel company built its own true resolution rate from internal data, and the bank running Sierra wants end-to-end resolution rather than deflection.
The vendors' own definitions matter here. Decagon's December 2024 pricing guide measures resolution rate as the share of conversations the AI agent resolves without needing to escalate to a human, a definition that, as worded, does not by itself confirm the customer's problem was solved. Sierra's December 2024 description of outcome-based pricing says the outcomes it charges for are agreed with each customer in advance. That definition matters most where a contract charges for resolved conversations or outcomes: what bills can differ from the buyer's own measure of solved, and two agreements can count the same conversation differently. Under per-conversation pricing, a conversation can bill whether or not it was resolved. In our reading, three things are worth keeping apart: the resolution rate a vendor reports, the rate the buyer verifies, and the unit that triggers a charge.
Is Sierra cheaper than Decagon? These interviews do not establish that, because no buyer compares the two on price or on equivalent contracts. For Sierra, the online marketplace says it “is not the most budget-friendly of the vendors” and that “our goal was not to find the cheapest AI vendor”; the health insurer says some rivals cost “significantly lower” but that Sierra was willing to negotiate; a hotel group evaluating it says what would improve it is “if it were cheaper”; and the hospitality company that declined it cites cost. On the other side, the restaurant chain piloted it at a discount, and the specialty retailer using it for conversational search calls the cost effective. For Decagon there is little to go on: the consumer brand's “very competitive pricing” and the pilot's cost structure that did not fit. These are past experiences with particular contracts, not current prices. The two lead with different pricing models, though the units overlap. Sierra emphasizes outcome-based pricing: its December 2024 explanation says the charge follows an agreed result, such as a resolved support conversation or a saved cancellation, with unresolved or escalated conversations in most cases not charged, and that routing or greeter work may suit pricing by conversation count instead. Decagon's explanation the same month offers a fixed rate for every incoming conversation or a higher rate for each fully resolved conversation, with no charge for escalations, and says the vast majority of its customers choose per-conversation. These are published models, not a matched comparison of current terms. Neither names a price on its website as of November 2026, and the Asian airline paid an implementation fee for its proof of concept. Our suggestion, not a practice buyers describe: ask about minimum commitments, platform and implementation fees, included usage and overage, and how reopened, repeated, transferred or partly completed conversations bill, then compare expected total spend at the same volume and the same independently checked resolution rate.
What buyers wish they'd known
Before picking Sierra
Buyers describe a responsive team and accurate answers, and then more running work than they expected: someone has to review conversations, fix journeys and connect back-end systems before the agent resolves a meaningful share of requests. One live buyer reports bugs, and several say it is not the cheapest option. Our recommendation, not a practice buyers describe: name the internal owner and the engineering time before signing, and agree in the contract which outcomes are charged and how a resolved conversation is verified.
Before picking Decagon
The praise comes from few live accounts, both under two years in, and one company that piloted it says delivery fell short of the pitch. One user found its analytics did not show a true resolution rate and built its own. Voice and actions such as refunds are plans at the brand that praises it most. Our recommendation, not a practice buyers describe: ask for references at your scale and in your channels, and check reported resolutions against your own repeat-contact data during a pilot.
Four questions the interviews raise
Live at many companies here, some for a year or more; one buyer chose it for its references among large brands.
Few live accounts here; the strongest checked a reference customer before buying.
Buyers describe weekly review, vendor fixes and a need for engineering and product skills.
One user's staff were trained to write prompts and check quality; nobody here describes the workload.
Buyers report containment; one pilot hinges on defining resolved rather than contained.
One user built its own true resolution rate from internal data.
Leads with outcomes agreed in advance, and may price some work by conversation. Ask which outcomes bill.
Per conversation, or a higher rate per resolution; most customers pick per conversation. Ask what each bills.
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Start my checkup →How the operating conditions differ
Recurring situations in the corpus, and where the evidence concentrates in each:
Pressures visible in these interviews
Which should a company choose, Sierra or Decagon? These interviews do not establish an overall winner. Sierra has the longer record here, live at many companies with a responsive team and accurate answers; Decagon has a few strong accounts, one of them glowing, and one pilot that went badly. The evidence does not show that either resolves more customer problems: the outcome figures buyers give are mostly containment and their own readings, the companies that weighed both reached different decisions, and no company reports moving from one to the other.
The complaints these interviews carry sit on both sides: Sierra's are the effort to run it, slow progress from containment to resolution, bugs at one company and cost; Decagon's are analytics that do not always show a true resolution rate and a pilot one company says over-promised.
Our recommendation, drawn from these accounts rather than a practice buyers describe, is to run both on your own recent conversations before choosing. Take a sample of real requests, from knowledge questions to refunds, order changes and cases that must reach a person. Before the pilot, define a verified resolution for each request type, including whether the back-end action completed and whether the customer contacted support again within a week, and score needless escalations and hand-off quality alongside it. Ask each vendor to report containment, resolution and QA results separately, and reconcile them against your own order, ticket and contact-history records: a QA pass, or a conversation that ended without escalation, is not on its own a solved problem. Name who will review conversations and fix journeys, and how much engineering time that takes. Then put the cost questions above to both at the volume you expect next year.
For the wider category, see what contact center software is, where buyers picking AI specialists over their platform's own AI are covered, and whether an ecommerce brand needs an AI chatbot; for other head-to-heads, see all comparisons.
Common questions
Sierra vs. Decagon: which is better?
These interviews do not establish a winner, and the evidence is uneven: Sierra is live at many more of the companies interviewed, while Decagon's side rests on a few accounts. Sierra buyers praise a collaborative vendor team, accurate answers and tools for finding where the agent falls short; their complaints are the effort it takes to run, slow progress from containing conversations to resolving them, bugs at one company and cost. Decagon's live users praise its support, its analytics and, at one, its pricing; one says its analytics do not always show a true resolution rate, and a company that piloted it says it over-promised. No buyer here has tested the two against each other on the same conversations.
What's the difference between Sierra and Decagon?
Both sell AI agents that handle customer requests across chat, voice and email ahead of, or instead of, a human agent, connecting to the helpdesk, CRM and other systems a company already runs to look up information and take actions. They can replace an existing customer-facing bot without replacing the ticketing or contact-center platform underneath. The differences in these interviews are in what buyers deployed and how far along they are, not in what either product can do. Sierra buyers describe chat and voice agents at retailers, a bank, health insurers and travel companies, some live for a year or more. Decagon's live users describe an agent answering knowledge questions, and one plans to add voice and actions such as refunds; Decagon also sells voice agents, which no live user here describes running. The two lead with different pricing models: Sierra emphasizes outcome-based pricing with outcomes agreed in advance, while Decagon offers a per-conversation or a per-resolution rate and says most of its customers choose per-conversation.
Do companies choose between Sierra and Decagon?
A few companies in these interviews weigh both, and they ended in different places. In 2025 a large online retailer evaluated Decagon, chose Sierra for chat self-service and later signed with it, while still weighing both for voice. A global hospitality company piloted Decagon, evaluated Sierra, and chose neither, deciding to build on its existing tools. A large North American bank has run Sierra for two years and is looking at Decagon among others for end-to-end resolution, with no shortlist. A US consumer bank is still researching both. That is too few companies to establish which vendor buyers prefer.
How do Sierra and Decagon price their AI agents?
Neither names a price on its website. Sierra's December 2024 description of its model says customers pay when the agent achieves an outcome agreed in advance, such as a resolved support conversation or a saved cancellation, that unresolved or escalated conversations are in most cases not charged, and that routing-style work may be billed by conversation count instead. Decagon's December 2024 description offers a fixed rate per incoming conversation, or a higher rate per fully resolved conversation with no charge for escalations, and says the vast majority of its customers choose per-conversation. The units overlap, since both describe charging for resolved conversations and for conversation counts, but each contract defines what bills, so the published models are not a matched comparison of current terms. Buyers here describe Sierra as not the cheapest, with discounts and negotiation at some companies; one Decagon user calls its pricing very competitive, and a company that piloted it says the cost structure did not fit.
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